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Quantum compiling by deep reinforcement learning

Computer Science

Quantum compiling by deep reinforcement learning

L. Moro, M. G. A. Paris, et al.

Discover a groundbreaking approach to quantum compiling through deep reinforcement learning (DRL), presented by Lorenzo Moro, Matteo G. A. Paris, Marcello Restelli, and Enrico Prati. This innovative method not only learns to approximate single-qubit unitaries efficiently but also drastically reduces execution time, potentially enabling real-time applications. Don't miss out on this cutting-edge research!

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Playback language: English
Abstract
Quantum compiling, approximating any unitary transformation as a sequence of gates from a finite universal set, faces a trade-off between sequence length, precompilation time, and execution time. Traditional methods are slow, unsuitable for real-time computation. This paper proposes a deep reinforcement learning (DRL) method requiring a single precompilation to learn a strategy for approximating single-qubit unitaries. The approach reduces execution time, improves the length-execution time trade-off, and enables potential real-time operations.
Publisher
Communications Physics
Published On
Aug 06, 2021
Authors
Lorenzo Moro, Matteo G. A. Paris, Marcello Restelli, Enrico Prati
Tags
quantum compiling
deep reinforcement learning
unitary transformations
execution time
real-time computation
single-qubit unitaries
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